Turn trusted data into
operational intelligence

Get the number that matters while the shift is still running, and compare one site against another without exporting anything first.

You cannot act on a number that arrives too late

Computed too late

The number gets calculated in a warehouse after the data lands. By the time anyone sees it, the shift it described is over and the output is gone.

Computed too far away

Answering one question means exporting from every site first. Nobody asks the small questions, because the cost of asking is a data project.

Two sets of books

The floor has its number and corporate has another, so the meeting gets spent arguing about whose figure is right instead of what caused it.

Analyze turns that structure into numbers you can manage by

Real-time computation & analytics

Derive the values your operation manages by, computed on the stream as data flows rather than after it lands somewhere else.

So you can

Calculate OEE and downtime once, consistently, near the line, so the floor and corporate argue about the cause instead of the number.

Event & anomaly detection

Watch for the conditions that matter to you and for the patterns that do not fit the established behavior of an asset.

So you can

See a deviation while the line is still running, so root cause takes minutes rather than days and problems get caught before they become scrap.

Distributed query & calculations

Ask a question once and have it answered where the data lives, across sites, without moving anything to a central store first.

So you can

Stop the decision loop depending on a cloud round trip, so latency-sensitive and sovereignty-constrained use cases become possible at all.

Operational benchmarks & baselines

Establish what normal looks like per asset, per line and per site, and measure live behavior against it continuously.

So you can

Know what normal looks like per asset, so a real problem and a noisy sensor stop looking identical and plants can be compared on terms that match.

What stops an agent acting on data that is wrong

Everyone in this category sells agents. Almost nobody says what happens when the input is bad. This is that answer, and it is what makes delegating anything defensible.

01 · Detect

It does not match the model

Wrong type, out of range, a missing field, unit drift, or a tag renamed underneath you.

02 · Contain

It stops at the injection point

Quarantined before anything consumes it. Downstream holds at the last good value rather than silently ingesting a bad one.

03 · Surface

A person sees it in time

Expected schema and actual payload side by side, with the broker and the device named, while the shift is still running.

04 · Record

What was true, and when

The input is kept with the decision that was made on it, which is what makes an automated action defensible afterwards.

This is not a historian, and it is not your BI stack

Your warehouse and your analytics tools keep doing what they do well. This is the intelligence that runs in the operational plane, on data that has already been governed, so decisions can be made at the speed the line moves.

We are built to sit underneath that stack, not to replace it. Supported paths into Snowflake, Databricks, Kafka, Kinesis, Pub/Sub and your historian, so what reaches them is already modeled and already checked.